Allowing for uncertainty due to missing data in meta-analysis - Part 2: Hierarchical models

IR White, NJ Welton, AM Wood, AE Ades, JPT Higgins

Research output: Contribution to journalArticle (Academic Journal)peer-review

35 Citations (Scopus)

Abstract

We propose a hierarchical model for the analysis of data from several randomized trials where some outcomes are missing. The degree of departure from a missing-at-random assumption in each arm of each trial is expressed by an informative missing odds ratio (IMOR). We require a realistic prior for the IMORs, including an assessment of the prior correlation between IMORs in different arms and in different trials. The model is fitted by Monte Carlo Markov Chain techniques. By applying the method in three different data sets, we show that it is possible to appropriately capture the extra uncertainty due to missing data, and we discuss in what circumstances it is possible to learn about the IMOR.
Translated title of the contributionAllowing for uncertainty due to missing data in meta-analysis - Part 2: Hierarchical models
Original languageEnglish
Pages (from-to)728 - 745
Number of pages18
JournalStatistics in Medicine
Volume27 (5)
DOIs
Publication statusPublished - Feb 2008

Bibliographical note

Publisher: Wiley

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